{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "import biosppy\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_gaussian_noise(signal):\n",
    "    noise=np.random.normal(0,0.05,186)\n",
    "    return (signal+noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv('/Users/aring/jupyter/differential-privacy/data/Arno/ECGDataDenoised/MUSE_20180111_155115_19000.csv',header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-165.33</td>\n",
       "      <td>-358.97</td>\n",
       "      <td>-121.710</td>\n",
       "      <td>270.25</td>\n",
       "      <td>-28.869</td>\n",
       "      <td>-231.16</td>\n",
       "      <td>448.39</td>\n",
       "      <td>636.52</td>\n",
       "      <td>618.45</td>\n",
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       "      <td>-315.16</td>\n",
       "      <td>-570.84</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>-336.81</td>\n",
       "      <td>-114.980</td>\n",
       "      <td>251.41</td>\n",
       "      <td>-23.883</td>\n",
       "      <td>-217.52</td>\n",
       "      <td>441.74</td>\n",
       "      <td>646.47</td>\n",
       "      <td>642.56</td>\n",
       "      <td>35.3890</td>\n",
       "      <td>-269.51</td>\n",
       "      <td>-532.21</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-315.56</td>\n",
       "      <td>-108.630</td>\n",
       "      <td>233.45</td>\n",
       "      <td>-19.126</td>\n",
       "      <td>-204.36</td>\n",
       "      <td>436.06</td>\n",
       "      <td>656.31</td>\n",
       "      <td>665.95</td>\n",
       "      <td>76.5720</td>\n",
       "      <td>-225.83</td>\n",
       "      <td>-495.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-123.74</td>\n",
       "      <td>-296.23</td>\n",
       "      <td>-103.090</td>\n",
       "      <td>217.23</td>\n",
       "      <td>-14.815</td>\n",
       "      <td>-192.29</td>\n",
       "      <td>432.27</td>\n",
       "      <td>666.14</td>\n",
       "      <td>688.05</td>\n",
       "      <td>113.5100</td>\n",
       "      <td>-186.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-112.57</td>\n",
       "      <td>-279.75</td>\n",
       "      <td>-98.611</td>\n",
       "      <td>203.53</td>\n",
       "      <td>-11.213</td>\n",
       "      <td>-181.87</td>\n",
       "      <td>431.08</td>\n",
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       "      <td>708.47</td>\n",
       "      <td>144.3000</td>\n",
       "      <td>-151.68</td>\n",
       "      <td>-432.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0       1        2       3       4       5       6       7       8   \\\n",
       "0 -165.33 -358.97 -121.710  270.25 -28.869 -231.16  448.39  636.52  618.45   \n",
       "1 -150.75 -336.81 -114.980  251.41 -23.883 -217.52  441.74  646.47  642.56   \n",
       "2 -136.69 -315.56 -108.630  233.45 -19.126 -204.36  436.06  656.31  665.95   \n",
       "3 -123.74 -296.23 -103.090  217.23 -14.815 -192.29  432.27  666.14  688.05   \n",
       "4 -112.57 -279.75  -98.611  203.53 -11.213 -181.87  431.08  676.31  708.47   \n",
       "\n",
       "         9       10      11  \n",
       "0   -7.8987 -315.16 -570.84  \n",
       "1   35.3890 -269.51 -532.21  \n",
       "2   76.5720 -225.83 -495.39  \n",
       "3  113.5100 -186.00 -461.84  \n",
       "4  144.3000 -151.68 -432.56  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead1=train_df[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x135acc6d8>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lead1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result=biosppy.signals.ecg.ecg(signal=lead1.values, sampling_rate=500, show=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[193,\n",
       " 419,\n",
       " 655,\n",
       " 905,\n",
       " 1147,\n",
       " 1382,\n",
       " 1624,\n",
       " 1858,\n",
       " 2094,\n",
       " 2325,\n",
       " 2557,\n",
       " 2817,\n",
       " 3053,\n",
       " 3287,\n",
       " 3507,\n",
       " 3743,\n",
       " 4012,\n",
       " 4249,\n",
       " 4491,\n",
       " 4724]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['rpeaks'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def partition(list_, indexes):\n",
    "    if indexes[0] != 0:\n",
    "        indexes = [0] + indexes\n",
    "    if indexes[-1] != len(list_):\n",
    "        indexes = indexes + [len(list_)]\n",
    "    return [ list_[a:b] for (a,b) in zip(indexes[:-1], indexes[1:])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_beats=partition(lead1.values.tolist(),result['rpeaks'].tolist())\n",
    "len(heart_beats[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-44.357,\n",
       " -48.898999999999994,\n",
       " -53.443999999999996,\n",
       " -57.85,\n",
       " -62.228,\n",
       " -67.01100000000001,\n",
       " -73.053,\n",
       " -81.79899999999999,\n",
       " -95.036,\n",
       " -112.01,\n",
       " -125.12,\n",
       " -128.48,\n",
       " -126.32,\n",
       " -124.9,\n",
       " -128.49,\n",
       " -139.99,\n",
       " -160.98,\n",
       " -191.06,\n",
       " -227.48,\n",
       " -265.57,\n",
       " -300.08,\n",
       " -326.82,\n",
       " -343.93,\n",
       " -351.88,\n",
       " -352.32,\n",
       " -346.87,\n",
       " -336.57,\n",
       " -322.27,\n",
       " -305.55,\n",
       " -288.56,\n",
       " -272.29,\n",
       " -256.68,\n",
       " -241.51,\n",
       " -226.69,\n",
       " -212.23,\n",
       " -198.09,\n",
       " -184.21,\n",
       " -170.44,\n",
       " -156.56,\n",
       " -142.29,\n",
       " -127.41,\n",
       " -111.97,\n",
       " -96.53200000000001,\n",
       " -82.131,\n",
       " -69.718,\n",
       " -59.637,\n",
       " -51.603,\n",
       " -45.058,\n",
       " -39.477,\n",
       " -34.459,\n",
       " -29.704,\n",
       " -24.956999999999997,\n",
       " -19.969,\n",
       " -14.5,\n",
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       " -33.330999999999996,\n",
       " -36.383,\n",
       " -40.199]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_beats[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13669a3c8>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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CmC8iI6yoRfVMQ7OdFz4+ytUD+zMxS4eMektAgPDNm3P429euITzYxjde3cGs369n5e7TNLbYrS5P9VJWHd2bBBQYY44BiMgyYC5wwKJ6VDctzyukrKaR380ba3UpfmlCZiyrv3M97+w5zdPrjvCtv+wkKjSQWaOSmT0qhYnZcXqGt+oyqz4pqUBhm8dFwGSLalHd1Nhi5/mPjpKbGcvVA/XiM1axBQhzx6Zy25gBrD9czt93n2bV3lKW5xVhCxCuSovmmkHxXDOoP+MzYwkNslldsvJRVgVBe2cd/UtHp4gsBBYCZGRkeKMm1UXL84ooqWrg13eN0RPIfIAtQJg2LJFpwxJpaLaz/eR5Nh6tYOPRszz/8VGe+bCA4MAAJmTEMn1EEnNGJ5MSHWZ12cqHiBXD0ETkauAnxpiZzsePARhjftne8rm5uSYvL8+LFaqOVDc0M+3JjxiUEMHrX52iQeDjahtb2Hb8HJ8WVPDJkQoOnakBYGJWLPdPzmDO6BRCAnVPoa8Ske3GmNzOlrNqj2AbkCMi2UAxMA+436JaVDc8+2EB5+qaeOm2ERoCvUBESOBnewsAR8trWbWnhDd3FvPd13fzs3fyeXByBl+Ymk1sv2CLq1VWsWSPAEBE5gC/A2zAEmPMEx0tq3sEvuFQaQ23Pv0Jd4xL5cl7rrK6HOUCh8Ow8ehZXt50grUHztAv2MaDV2fylesGEh8RYnV5vU6L3UFVfTPn65qprGuisq6Z83VNGCA82EZYkI2wYBtx/YJJigwlJjzIK39IdXWPwLIg6A4NAuvZHYZ7/riR4xUXWPf9G4nTvx77jEOlNTzzYQHv7DlNSGAAX5iazSPTBtOvl4w6MsZQdL6eXYWVHCqtobymkYraRprsDoJsAQQGCBGhgUSFBhEVGkhkaBBRYa0/Q4MCaGoxNNsdn92a7IbGZjsNzXYaWxw0NNtpaHb86+OW1i/+yromzl9oorqheyf3hQYFMDQpkhEDohiXHsuNwxJIjAx1+7bRIFBu9dxHBfz36kM8de9V3KkXpu+TjpbX8sw/Clixs5iU6FAev3U4t45O8ckuQGMMOwsrWbnrNO/tK+FMdSPQeuA8rl8w8REhhAQG0OJw0NxiqG1sobqhmdrGFrrzlScCoYE2QoMCCHH+DA2yERIYQFRYELHhwcSGBxHT5mdM+MX2YESgvtlOfZOduiY75y40caa6geLKevJLqtl/upqq+mYCBG4alsTXbxzIhEz3nZejQaDcZndhJXc9v5FbRibx7P3jffKLQbnP9pPn+NFb+zlQUs1tY1L45Z2jiQwNsrosoLUL5u97TvPch0c5UlZLcGAANw5J4LqceMamxzIsJZIgW8fnyTochtqmFmoaWqhpaKah2UGQTQgJDCDI1noLtAmhQTZCA20E2cSjn3djDAdLa3hnz2mWbS3k7IUmbh2Twk9vH0l/N3TRaRAotyitamDusxsIDAjg3W9dS0y4dgn5A7vD8MePj/LU2sOkxoTxzP3jGJMWY1k9xhj+vqeEJ9ccpPBcPUOTIvnSddnMGpVMlI+ElKvqmlr40/rjPPthAVFhQTz3wHgmuXihJw0C5bL6Jjv3Ld7E0bJa/vb1axieEmV1ScrL8k6c41t/2UnFhSZ+e+9Ybh2T4vUaDpXW8OOV+9h87BwjUqL43owh3DQskYCAvrlnerC0mq+/soOi83U8de9YPnfVgB6/VleDQGcfVe1yOAw/+Ntu9hZX8ft54zQE/FRuVhzvfOs6RqdGs+i1Hbzw8VGvTYHdYnfw7IcF3PaHTzhYWsMTd4zi79+8lukjkvpsCAAMS47irW9MZVx6LN9etpM3dxR5fJ0aBOoyxhj+c+U+3t1TwqOzhjF9RJLVJSkLxfUL5tUvT+bW0Sn88r2D/OjtfbTYHR5d5/GKC9zzwiaeXHOImSOT+fD7N/LA5ExsfTgA2ooOD+KlL05kysD+vLbllMevWd07xof10NnaRh5fsY9v3jyYkQOirS6nVzDG8KvVB3ll8ym+esNAFl4/0OqSlA8IDbLxh/njSIsL44WPj3G6soGn549z+8R2xhiW5xXyX38/QGCA8Pt5Y5k71j8vgRoeHMiShyfSZHd4PAD79B5BgAg7Tp3n28t2Ud+kU/R2xbMfFvDCx8d4cEoGj84apiOE1GcCAoTHZg/n558fxceHy7nnj5soqap32+ufv9DE117Zzn+8sZex6TGs+e71fhsCF4UG2bxyMLxPB0Fsv2D+596rKCir5Zfv5Vtdjs97ZfNJfvP+Ye4Yl8pPbx+lIaDa9eCUTJY8PJHCc3XMfeZTdhVWuvR6xhjW7C9l5u/W8+HBch6fM5xXvjRZJ8bzoj4dBADX5STwpWuzWbrpJB8eLLO6HJ/17p4SfvT2Pm4alsh/3z2mTx+MU667YUgCb3z9GoIDA7jnjxv530+O9agfe3dhJfct3sxX/7yd2PBgViy6hq9cP1A/f17mF8NHG5rtfP7ZT6mobWT1d67XuVQuseXYWR58cQtj02NY+sXJhAXrbJSqayrrmvjBX/fwQf4ZxmXE8Njs4Z2Ofa+obeQf+WW8ubOIzcfO0b9fMN+dMYR5E9MJvMLJYKr79DyCSxwqreFzz2zg2sHxvLggV7s9nEqrGrjtD58QFRbEim9MJTqsb5yco7zHGMNbu4r5xaqDlNc0Mjo1mlmjkhmVGk1ceDCNLXZOnK1j/+kqdhdWsrOwEmMgPS6M+ydl8uCUDJ85c7mv0SBox5INx/npOwf45Z2jmT9JL3bT1OLgvsWbOFRaw9uLppKTFGl1SaoXq2+y8/q2U/xtRxH7iqsvez4syMbwlEiuy0nglpFJjEiJ0j/IPMzXr0dgiYevyWLtgTP84t18bhya4PcHo37+7gF2nqrk2fvHawgol4UF23h4ajYPT82msq6Jw2dqqapvJiQwgAExoWTHR/jNeQC9jV91yAUECL+6azTNDgf/tfKA1eVY6sODZSzddJIvX5ttybQBqm+LCQ9mUnYcM0Ykcf2QBAYnRmoI+DC/CgKAzP79eGTaYFbvL2XLsbNWl2OJ6oZmHntzL0OSIvh/s4ZaXY5SymJ+FwQAX7p2ICnRoTyxKt9r86b4kqfeP8yZmgaevPsqvV6tUso/gyAs2MZ3puewp6iKTX62V5BfUs3STSd4YHIGV6VbN62wUsp3+GUQAMwdm0pseBAvbzxhdSle9YtV+USFBfGDW7RLSCnVym+DIDTIxrxJGaw9cIai83VWl+MVG49W8MmRCh6ZNlgvMKOU+ozfBgHA/ZMycBh4b2+p1aV4nDGG36w5REp0KA9OybS6HKWUD/HrIEiPC2dYciTrDp6xuhSP23TsLDtOVbJo2mBCg/QAsVLqn1wKAhG5R0T2i4hDRHIvee4xESkQkUMiMrNN+yxnW4GIPOrK+t1h2rBE8k6cp6q+2epSPOr5j44SHxHC3RPSrC5FKeVjXN0j2AfcCaxv2ygiI4B5wEhgFvCciNhExAY8C8wGRgDzncta5uZhibQ4DJ8cKbeyDI/KL6nmkyMVfPHaLN0bUEpdxqUgMMbkG2MOtfPUXGCZMabRGHMcKAAmOW8FxphjxpgmYJlzWcuMy4glJjyIjw713SB4fVshwbYA7tf5lZRS7fDUMYJUoLDN4yJnW0ftlxGRhSKSJyJ55eWe+5K2BQgTs+LYfvK8x9ZhpcYWO2/tKmbGyCQdKaSUalenQSAiH4jIvnZuV/pLvr1JRcwV2i9vNGaxMSbXGJObkJDQWZkumZAZy/GKC5ytbfToeqzwwYEyKuuauTc33epSlFI+qtPZR40x03vwukVA22+eNOC0835H7ZaZkBkLwI5TlcwYkWRxNe61PK+QlOhQrh0cb3UpSikf5amuoZXAPBEJEZFsIAfYCmwDckQkW0SCaT2gvNJDNXTZ6NRogmzS57qHSqrqWX+knLsnpOnMj0qpDrl0PQIRuQP4A5AAvCsiu4wxM40x+0VkOXAAaAEWGWPszt95BFgD2IAlxpj9Lr0DNwgNsjFyQDQ7+lgQvLmjGGPQIaNKqStyKQiMMSuAFR089wTwRDvtq4BVrqzXE3IzY1m6+SQNzfY+McTSGMPyvEKmDIwjs38/q8tRSvkwvz6zuK2pOfE0tTjYcvyc1aW4xdbj5zh5tk4PEiulOqVB4DQluz/BgQGsP9w3zidYnldEREggs0fp1ceUUlemQeAUFmxjUlbcvwRB0fk6fr36IG/tLKaxxW5hdd1T29jCqr0lfO6qFMKCe383l1LKs/zq4vWduWFIAk+syufU2TrOXmjkK0u3U+E8t2DbiQyeuGO0xRV2zbt7TlPfbOce7RZSSnWB7hG0MXNkMiGBAcz/02bmLd5MWHAA73/3eu6ekMYbO4qorGuyusQuWZ5XxODECMbpFciUUl2gQdBGRv9wXvvKFOqb7YxOjeatb0xlSFIkX74um4ZmB69vK+z8RSxWUFbL9pPnuTc3DRE9d0Ap1TntGrrEhMxYNj56E8G2AAKcJ2ENS45iUnYcy/MK+eoNgyyu8Mr+ur0QW4Bwxzg9d0Ap1TW6R9CO0CDbZyFw0Q1DEjhafoGqOt+9bkGz3cEb24u5aVgiCZEhVpejlOolNAi6aExaNAD7TldZXEnHPj5UTkVtI/fomcRKqW7QIOiiMamtB153F1VaXEnHlucVEh8RzLRhiVaXopTqRTQIuig6PIjM/uHsLfLNPYIz1Q2sO1jGXePTCLLpP6tSquv0G6MbRqdGs8dHg2D5tkLsDsN8vQqZUqqbNAi6YUxaNMWV9T53ARu7w7BsWyFTB/cnK14nmFNKdY8GQTcMS44C4EhZrcWV/Kv1h8sprqzn/kmZVpeilOqFNAi6IbN/OACnztZZXMm/enXLKeIjgvvc1dWUUt6hQdANA2LCsAUIp875ThCUVNXzj4NnuCc3neBA/edUSnWffnN0Q5AtgNSYME76UBC8vq0Qh4H5E/UgsVKqZzQIuikjLpxTZy9YXQYALfbW+Y+uy4knw9ltpZRS3aVB0E0Z/cN9pmvoo0PllFQ18MBkPUislOo5DYJuyogL53xdM9UN1s859OqWkyRGhnDzcD2TWCnVcxoE3ZQZ5xsjh4rO1/HR4XLum5iuZxIrpVzi0jeIiDwpIgdFZI+IrBCRmDbPPSYiBSJySERmtmmf5WwrEJFHXVm/FS72xZ+0OAhe31aIAPP0TGKllItc/VNyLTDKGDMGOAw8BiAiI4B5wEhgFvCciNhExAY8C8wGRgDzncv2GhkX9wgsPE7Q7DxIfOPQRFJjwiyrQynVN7gUBMaY940xLc6Hm4GL8x/PBZYZYxqNMceBAmCS81ZgjDlmjGkCljmX7TUiQ4OICg2kpKreshrW5Z+hrKaR+3VvQCnlBu7sXP4i8J7zfirQ9rqORc62jtp7lZToMEqqGixb/6tbTpESHcqNQxMsq0Ep1Xd0eqlKEfkASG7nqceNMW87l3kcaAFevfhr7SxvaD94TAfrXQgsBMjI8K2/fJOjQym1KAhOna3jkyMVfHf6EAL1ILFSyg06DQJjzPQrPS8iC4DbgJuNMRe/1IuA9DaLpQGnnfc7ar90vYuBxQC5ubnthoVVkqNCOVBSbcm6/7a9kACBeyfqVciUUu7h6qihWcB/ALcbY9oePV0JzBOREBHJBnKArcA2IEdEskUkmNYDyitdqcEKydGhVNQ20tTi8Op6HQ7DmzuLmTo4npRoPUislHIPV/sWngEigbUisktE/ghgjNkPLAcOAKuBRcYYu/PA8iPAGiAfWO5ctldJiQ7FGCir8W73UN7J8xSdr+fO8b3usIpSyod12jV0JcaYwVd47gngiXbaVwGrXFmv1ZKjQwEorWogLdZ7c/ys2FlEeLCNmSPbO2SjlFI9o0cbe+CzIKj23h5BQ7Odd/aUMGtUMuHBLuW3Ukr9Cw2CHkiJau2f9+bIoXX5ZdQ0tHDnOD1IrJRyLw2CHogKCyQsyObVcwlW7CwiKSqEqwf199o6lVL+QYOgB0SElOhQr3UNVdY18dGhcj4/NhVbQHunaCilVM9pEPRQcnQoJZXemWZiXX4ZLQ7DnNEpXlmfUsq/aBD0UHJUKGeqG72yrjX7S0mJDmVMWrRX1qeU8i/UCV5JAAAMEElEQVQaBD2UGBVKWU0D/zyZ2jPqmlr4+HA5M0cmI6LdQkop99Mg6KGkqBCa7YbzdZ69Utn6w+U0tji4ZWSSR9ejlPJfGgQ9lBTVei7BGQ8fMF69r5TY8CAmZcV5dD1KKf+lQdBDSVEhgGeDoKnFwbqDZUwfnqQzjSqlPEa/XXooMbJ1j6DMgweMNx07S01Di04poZTyKA2CHkr0wh7Bmv2lhAfbuDYn3mPrUEopDYIeCgm0ERsexBkPzUBqdxje33+GaUMTCQ2yeWQdSikFGgQuSfLguQQ7T52norZRRwsppTxOg8AFiVGhlHmoa2j1vlKCbQHcNCzRI6+vlFIXaRC4ICkyxCN7BMYY1hwo5ZrB/YkMDXL76yulVFsaBC5IigqlvLYRu8O9Zxfnl9RQeK6eWTpaSCnlBRoELkiKCsHuMJy94N69gtX7SwkQmD5Cjw8opTxPg8AFiVGeOZfg/f2l5GbGER8R4tbXVUqp9mgQuMAT00ycqLjAwdIaZo7SbiGllHdoELjgn9NMuG+PYM3+UgBu0W4hpZSXaBC4ID4iBBH37hGs3l/KqNQo0uPC3faaSil1JS4FgYj8TET2iMguEXlfRAY420VEnhaRAufz49v8zgIROeK8LXD1DVgpyBZA/34hlLnp7OIz1Q3sPFXJzBHaLaSU8h5X9wieNMaMMcaMBd4B/tPZPhvIcd4WAs8DiEgc8GNgMjAJ+LGIxLpYg6WSotx3LsH7zm4hPT6glPIml4LAGFPd5mE/4OKA+rnAUtNqMxAjIinATGCtMeacMeY8sBaY5UoNVmudZsI9ewTv7StlYHw/chIj3PJ6SinVFS4fIxCRJ0SkEHiAf+4RpAKFbRYrcrZ11N5ruWuPoKK2kc3HzjJndIpeklIp5VWdBoGIfCAi+9q5zQUwxjxujEkHXgUeufhr7byUuUJ7e+tdKCJ5IpJXXl7etXdjgcTIUM5eaKTZ7nDpddbsL8VhYM7oFDdVppRSXdNpEBhjphtjRrVze/uSRV8D7nLeLwLS2zyXBpy+Qnt7611sjMk1xuQmJCR09f14XVJUKMa0/kXvilV7S8iO78fwlEg3VaaUUl3j6qihnDYPbwcOOu+vBB5yjh6aAlQZY0qANcAtIhLrPEh8i7Ot13LHuQRnaxvZdPQsc0Yna7eQUsrrAl38/V+JyFDAAZwEvuZsXwXMAQqAOuALAMaYcyLyM2Cbc7mfGmPOuViDpdxxdvGa/WdwGLh19AB3laWUUl3mUhAYY+7qoN0Aizp4bgmwxJX1+hJ3XLJSu4WUUlbSM4tdFN8vhCCbUFLVsyA4d6GJTce0W0gpZR0NAhcFBAjJ0aEUn6/v0e+vPVCK3WGYPUpHCymlrKFB4AapMWEUV/YsCFbvKyUtNoyRA6LcXJVSSnWNBoEbpMaEc7oHQVDT0MynBWeZNVK7hZRS1tEgcIPUmNZpJrp7UtlHh8ppsjt0biGllKU0CNwgNTYMh4HSbh4wXr2/lPiIYMZn9Op595RSvZwGgRukxrReO6A7xwkamu18dLCMGSOSsQVot5BSyjoaBG4wIKb1pLLujBzaeLSCC012Zo7UK5EppaylQeAGA2LCALp1wHj1vlIiQwK5ZlC8p8pSSqku0SBwg9AgG/ERIV3uGnI4DP84WMa0YYkEB+o/gVLKWvot5CapsV0/l+BgaQ0VtU3cMMR3Z1VVSvkPDQI3yYwL53jFhS4tu6Gg9foKUwdrt5BSynoaBG4yODGC4sp66pvsnS67oeAsOYkRJEeHeqEypZS6Mg0CNxmUEIExcKyi9orLNTTb2Xr8rO4NKKV8hgaBmwx2XnC+oOzKQbDj5Hkamh1cl6NBoJTyDRoEbpIVH06AwNHyKx8n2FBQQWCAMHlgfy9VppRSV6ZB4CYhgTYy4sI52skewYaCCsZlxBAR4urF4ZRSyj00CNxoUELEFbuGzl9oYm9xFdcO1mGjSinfoUHgRoMTIzhecQG7w7T7/KZjZzEGrs3RbiGllO/QIHCjIUmRNNkdHCmraff5jw6VERkayFVpMV6uTCmlOqZB4EaTsuMA2Hr83GXPtU4rUc4NQxIItOlmV0r5Dv1GcqO02DAGRIey5djlQbC7qJKK2kamD9fZRpVSvsUtQSAiPxARIyLxzsciIk+LSIGI7BGR8W2WXSAiR5y3Be5Yv68QaR0WuuX4OYz51+ME6/LLCBC4cageKFZK+RaXg0BE0oEZwKk2zbOBHOdtIfC8c9k44MfAZGAS8GMR6VOX55qUHUdFbSPH2sw7ZIzhvX0l5GbGERMebGF1Sil1OXfsEfwW+Heg7Z/Ac4GlptVmIEZEUoCZwFpjzDljzHlgLTDLDTX4jCnOE8X+kV/2WdsnRyo4Wn6Be3LTrCpLKaU65FIQiMjtQLExZvclT6UChW0eFznbOmpv77UXikieiOSVl5e7UqZXZcf3Y1J2HC9uOE5jS+sEdC9uOE58RAi3jx1gcXVKKXW5Tk9vFZEPgOR2nnoc+CFwS3u/1k6buUL75Y3GLAYWA+Tm5rY/MN9HPTJtMA8t2cqfN50kITKEjw+X870ZQwgJtFldmlJKXabTIDDGTG+vXURGA9nAbhEBSAN2iMgkWv/ST2+zeBpw2tl+4yXtH/Wgbp92XU48E7Ni+fm7+QBMGRjHl6/LtrgqpZRqX48nvDHG7AUSLz4WkRNArjGmQkRWAo+IyDJaDwxXGWNKRGQN8Is2B4hvAR7rcfU+SkRY+sXJvLWrmKNltXz/lqGEBevegFLKN3lq5rNVwBygAKgDvgBgjDknIj8DtjmX+6kx5vJB931AWLCN+ZMyrC5DKaU65bYgMMZktblvgEUdLLcEWOKu9SqllHKNnlmslFJ+ToNAKaX8nAaBUkr5OQ0CpZTycxoESinl5zQIlFLKz2kQKKWUn5NL5833RSJSDpx04SXigQo3ldOb6XbQbXCRbodWfX07ZBpjOr0ISq8IAleJSJ4xJtfqOqym20G3wUW6HVrpdmilXUNKKeXnNAiUUsrP+UsQLLa6AB+h20G3wUW6HVrpdsBPjhEopZTqmL/sESillOpAnw4CEZklIodEpEBEHrW6Hm8SkRMisldEdolInrMtTkTWisgR58/Yzl6ntxGRJSJSJiL72rS1+76l1dPOz8ceERlvXeXu1cF2+ImIFDs/E7tEZE6b5x5zbodDIjLTmqrdS0TSReRDEckXkf0i8m1nu999HjrTZ4NARGzAs8BsYAQwX0RGWFuV100zxoxtMzzuUWCdMSYHWOd83Ne8BMy6pK2j9z0byHHeFgLPe6lGb3iJy7cDwG+dn4mxxphVAM7/F/OAkc7fec75/6e3awG+b4wZDkwBFjnfqz9+Hq6ozwYBMAkoMMYcM8Y0AcuAuRbXZLW5wMvO+y8Dn7ewFo8wxqwHLr3qXUfvey6w1LTaDMSISIp3KvWsDrZDR+YCy4wxjcaY47ReWXCSx4rzEmNMiTFmh/N+DZAPpOKHn4fO9OUgSAUK2zwucrb5CwO8LyLbRWShsy3JGFMCrf9JaHPN6T6uo/ftj5+RR5zdHkvadA32+e0gIlnAOGAL+nm4TF8OAmmnzZ+GSE01xoyndXd3kYhcb3VBPsjfPiPPA4OAsUAJ8D/O9j69HUQkAngD+I4xpvpKi7bT1me2w5X05SAoAtLbPE4DTltUi9cZY047f5YBK2jd1T9zcVfX+bPMugq9qqP37VefEWPMGWOM3RjjAP7EP7t/+ux2EJEgWkPgVWPMm85m/Txcoi8HwTYgR0SyRSSY1oNhKy2uyStEpJ+IRF68D9wC7KP1/S9wLrYAeNuaCr2uo/e9EnjIOVpkClB1scugL7qkv/sOWj8T0Lod5olIiIhk03qwdKu363M3ERHgRSDfGPNUm6f083ApY0yfvQFzgMPAUeBxq+vx4vseCOx23vZffO9Af1pHSRxx/oyzulYPvPe/0Nrt0UzrX3hf6uh909oV8Kzz87EXyLW6fg9vhz873+ceWr/0Utos/7hzOxwCZltdv5u2wbW0du3sAXY5b3P88fPQ2U3PLFZKKT/Xl7uGlFJKdYEGgVJK+TkNAqWU8nMaBEop5ec0CJRSys9pECillJ/TIFBKKT+nQaCUUn7u/wPA/OkEIR4RPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(heart_beats[10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "187",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 187",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-ad691697c827>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m187\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m187\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf_3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m187\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf_4\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m187\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 187"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "df_1=train_df[train_df[187]==1]\n",
    "df_2=train_df[train_df[187]==2]\n",
    "df_3=train_df[train_df[187]==3]\n",
    "df_4=train_df[train_df[187]==4]\n",
    "df_0=(train_df[train_df[187]==0]).sample(n=20000,random_state=42)\n",
    "\n",
    "df_1_upsample=resample(df_1,replace=True,n_samples=20000,random_state=123)\n",
    "df_2_upsample=resample(df_2,replace=True,n_samples=20000,random_state=124)\n",
    "df_3_upsample=resample(df_3,replace=True,n_samples=20000,random_state=125)\n",
    "df_4_upsample=resample(df_4,replace=True,n_samples=20000,random_state=126)\n",
    "\n",
    "train_df=pd.concat([df_0,df_1_upsample,df_2_upsample,df_3_upsample,df_4_upsample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=train_df.groupby(187,group_keys=False).apply(lambda train_df : train_df.sample(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I use a fonction ( will depend of the version) where i add a noise to the data to generilize my train.\n",
    "\n",
    "tempo=c.iloc[0,:186]\n",
    "bruiter=add_gaussian_noise(tempo)\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(c.iloc[0,:186])\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(bruiter)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train_df[187]\n",
    "target_test=test_df[187]\n",
    "y_train=to_categorical(target_train)\n",
    "y_test=to_categorical(target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train_df.iloc[:,:186].values\n",
    "X_test=test_df.iloc[:,:186].values\n",
    "\n",
    "X_train = X_train.reshape(len(X_train), X_train.shape[1],1)\n",
    "X_test = X_test.reshape(len(X_test), X_test.shape[1],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.1627907 ],\n",
       "        [0.54069769],\n",
       "        [0.75581396],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.99006623],\n",
       "        [0.93874174],\n",
       "        [0.34437087],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.97423887],\n",
       "        [0.93208432],\n",
       "        [0.59016395],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.79393941],\n",
       "        [0.69090909],\n",
       "        [0.57727271],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.97739506],\n",
       "        [0.93864369],\n",
       "        [0.88912809],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]],\n",
       "\n",
       "       [[0.87138265],\n",
       "        [0.6141479 ],\n",
       "        [0.55305469],\n",
       "        ...,\n",
       "        [0.        ],\n",
       "        [0.        ],\n",
       "        [0.        ]]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def network(X_train,y_train,X_test,y_test):\n",
    "    im_shape=(X_train.shape[1],1)\n",
    "    inputs_cnn=Input(shape=(im_shape), name='inputs_cnn')\n",
    "    conv1_1=Convolution1D(64, (6), activation='relu', input_shape=im_shape)(inputs_cnn)\n",
    "    conv1_1=BatchNormalization()(conv1_1)\n",
    "    pool1=MaxPool1D(pool_size=(3), strides=(2), padding=\"same\")(conv1_1)\n",
    "    conv2_1=Convolution1D(64, (3), activation='relu', input_shape=im_shape)(pool1)\n",
    "    conv2_1=BatchNormalization()(conv2_1)\n",
    "    pool2=MaxPool1D(pool_size=(2), strides=(2), padding=\"same\")(conv2_1)\n",
    "    conv3_1=Convolution1D(64, (3), activation='relu', input_shape=im_shape)(pool2)\n",
    "    conv3_1=BatchNormalization()(conv3_1)\n",
    "    pool3=MaxPool1D(pool_size=(2), strides=(2), padding=\"same\")(conv3_1)\n",
    "    flatten=Flatten()(pool3)\n",
    "    dense_end1 = Dense(64, activation='relu')(flatten)\n",
    "    dense_end2 = Dense(32, activation='relu')(dense_end1)\n",
    "    main_output = Dense(5, activation='softmax', name='main_output')(dense_end2)\n",
    "    \n",
    "    \n",
    "    model = Model(inputs= inputs_cnn, outputs=main_output)\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy',metrics = ['accuracy'])\n",
    "    \n",
    "    \n",
    "    callbacks = [EarlyStopping(monitor='val_loss', patience=8),\n",
    "             ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]\n",
    "\n",
    "    history=model.fit(X_train, y_train,epochs=40,callbacks=callbacks, batch_size=32,validation_data=(X_test,y_test))\n",
    "    model.load_weights('best_model.h5')\n",
    "    return(model,history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(history,X_test,y_test,model):\n",
    "    scores = model.evaluate((X_test),y_test, verbose=0)\n",
    "    print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n",
    "    \n",
    "    print(history)\n",
    "    fig1, ax_acc = plt.subplots()\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Model - Accuracy')\n",
    "    plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "    plt.show()\n",
    "    \n",
    "    fig2, ax_loss = plt.subplots()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Model- Loss')\n",
    "    plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.show()\n",
    "    target_names=['0','1','2','3','4']\n",
    "    \n",
    "    y_true=[]\n",
    "    for element in y_test:\n",
    "        y_true.append(np.argmax(element))\n",
    "    prediction_proba=model.predict(X_test)\n",
    "    prediction=np.argmax(prediction_proba,axis=1)\n",
    "    cnf_matrix = confusion_matrix(y_true, prediction)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 100000 samples, validate on 21892 samples\n",
      "Epoch 1/40\n",
      "100000/100000 [==============================] - 78s 780us/step - loss: 0.1900 - acc: 0.9319 - val_loss: 0.1788 - val_acc: 0.9374\n",
      "Epoch 2/40\n",
      "100000/100000 [==============================] - 83s 826us/step - loss: 0.0728 - acc: 0.9751 - val_loss: 0.1179 - val_acc: 0.9637\n",
      "Epoch 3/40\n",
      "100000/100000 [==============================] - 80s 798us/step - loss: 0.0485 - acc: 0.9839 - val_loss: 0.1216 - val_acc: 0.9629\n",
      "Epoch 4/40\n",
      "100000/100000 [==============================] - 70s 700us/step - loss: 0.0375 - acc: 0.9877 - val_loss: 0.1270 - val_acc: 0.9640\n",
      "Epoch 5/40\n",
      "100000/100000 [==============================] - 69s 687us/step - loss: 0.0305 - acc: 0.9902 - val_loss: 0.1211 - val_acc: 0.9682\n",
      "Epoch 6/40\n",
      "100000/100000 [==============================] - 70s 698us/step - loss: 0.0249 - acc: 0.9920 - val_loss: 0.1897 - val_acc: 0.9505\n",
      "Epoch 7/40\n",
      "100000/100000 [==============================] - 73s 730us/step - loss: 0.0212 - acc: 0.9933 - val_loss: 0.1102 - val_acc: 0.9743\n",
      "Epoch 8/40\n",
      "100000/100000 [==============================] - 75s 748us/step - loss: 0.0179 - acc: 0.9945 - val_loss: 0.1245 - val_acc: 0.9722\n",
      "Epoch 9/40\n",
      "100000/100000 [==============================] - 70s 699us/step - loss: 0.0171 - acc: 0.9947 - val_loss: 0.1111 - val_acc: 0.9780\n",
      "Epoch 10/40\n",
      "100000/100000 [==============================] - 75s 748us/step - loss: 0.0148 - acc: 0.9956 - val_loss: 0.1262 - val_acc: 0.9736\n",
      "Epoch 11/40\n",
      "100000/100000 [==============================] - 69s 686us/step - loss: 0.0137 - acc: 0.9959 - val_loss: 0.1088 - val_acc: 0.9797\n",
      "Epoch 12/40\n",
      "100000/100000 [==============================] - 69s 688us/step - loss: 0.0121 - acc: 0.9962 - val_loss: 0.1279 - val_acc: 0.9760\n",
      "Epoch 13/40\n",
      "100000/100000 [==============================] - 69s 694us/step - loss: 0.0114 - acc: 0.9965 - val_loss: 0.1207 - val_acc: 0.9774\n",
      "Epoch 14/40\n",
      "100000/100000 [==============================] - 69s 687us/step - loss: 0.0109 - acc: 0.9968 - val_loss: 0.1412 - val_acc: 0.9709\n",
      "Epoch 15/40\n",
      "100000/100000 [==============================] - 69s 689us/step - loss: 0.0108 - acc: 0.9968 - val_loss: 0.1303 - val_acc: 0.9769\n",
      "Epoch 16/40\n",
      "100000/100000 [==============================] - 69s 690us/step - loss: 0.0079 - acc: 0.9974 - val_loss: 0.1313 - val_acc: 0.9783\n",
      "Epoch 17/40\n",
      "100000/100000 [==============================] - 70s 702us/step - loss: 0.0100 - acc: 0.9972 - val_loss: 0.1265 - val_acc: 0.9788\n",
      "Epoch 18/40\n",
      "100000/100000 [==============================] - 69s 686us/step - loss: 0.0072 - acc: 0.9977 - val_loss: 0.1644 - val_acc: 0.9726\n",
      "Epoch 19/40\n",
      "100000/100000 [==============================] - 69s 687us/step - loss: 0.0086 - acc: 0.9976 - val_loss: 0.1271 - val_acc: 0.9800\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense, Convolution1D, MaxPool1D, Flatten, Dropout\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "import keras\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "model,history=network(X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.97%\n",
      "<keras.callbacks.History object at 0x13d1ea048>\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'accuracy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-e4dd497b4d79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevaluate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-27-c7f81c0b7d46>\u001b[0m in \u001b[0;36mevaluate_model\u001b[0;34m(history, X_test, y_test, model)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mfig1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'accuracy'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(history,X_test,y_test,model)\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-8070a62e65b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;31m# Compute confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mcnf_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_printoptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(10, 10))\n",
    "plot_confusion_matrix(cnf_matrix, classes=['N', 'S', 'V', 'F', 'Q'],normalize=True,\n",
    "                      title='Confusion matrix, with normalization')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sources\n",
    "https://www.kaggle.com/gregoiredc/arrhythmia-on-ecg-classification-using-cnn/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
